Travelled to:1 × Finland
1 × Ireland
2 × Canada
2 × Germany
6 × USA
Collaborated with:N.Indurkhya V.Seshadri R.Sasisekharan F.Damerau N.K.Verma S.J.Hong A.Dhurandhar R.J.Baseman C.Apté S.J.Buckley S.Kapoor S.Damgaard T.Zhang
Talks about:rule (3) mine (3) data (3) base (3) regress (2) predict (2) ensembl (2) learn (2) text (2) lightweight (1)
Person: Sholom M. Weiss
DBLP: Weiss:Sholom_M=
Contributed to:
201320032002
20022001
2001
200019991995199419941994Wrote 12 papers:
- KDD-2013-WeissDB #predict #quality
- Improving quality control by early prediction of manufacturing outcomes (SMW, AD, RJB), pp. 1258–1266.
- KDD-2003-WeissBKD #data mining #knowledge-based #mining
- Knowledge-based data mining (SMW, SJB, SK, SD), pp. 456–461.
- KDD-2002-WeissV #realtime
- A system for real-time competitive market intelligence (SMW, NKV), pp. 360–365.
- SIGIR-2002-DamerauZWI #categorisation
- Experiments in high-dimensional text categorization (FD, TZ, SMW, NI), pp. 357–358.
- KDD-2001-IndurkhyaW #classification #problem #rule-based
- Solving regression problems with rule-based ensemble classifiers (NI, SMW), pp. 287–292.
- MLDM-2001-IndurkhyaW #rule-based
- Rule-Based Ensemble Solutions for Regression (NI, SMW), pp. 62–72.
- ICML-2000-WeissI #induction #lightweight
- Lightweight Rule Induction (SMW, NI), pp. 1135–1142.
- MLDM-1999-HongW #data mining #mining #predict
- Advanced in Predictive Data Mining Methods (SJH, SMW), pp. 13–20.
- KDD-1995-SeshadriSW #data mining #feature model #mining
- Feature Extraction for Massive Data Mining (VS, RS, SMW), pp. 258–262.
- ICML-1994-WeissI
- Small Sample Decision tree Pruning (SMW, NI), pp. 335–342.
- KDD-1994-SasisekharanSW #machine learning #maintenance #network #using
- Proactive Network Maintenance Using Machine Learning (RS, VS, SMW), pp. 453–462.
- SIGIR-1994-ApteDW #automation #categorisation #independence #learning #modelling #towards
- Towards Language Independent Automated Learning of Text Categorisation Models (CA, FD, SMW), pp. 23–30.